Any image acquisition process is inevitably affected by noise, which intensity is a function of acquisition parameters. In radiography, for example, in order to minimize the harmful effects of radiation on the patient, it is necessary to reduce the dose and/or the time of exposure. As the result, obtained low-dose images suffer from noise, which hinders accurate diagnostics, and it becomes paramount to reduce its effect with digital post-processing. This problem is especially important in applications, where multiple images need to be acquired in series during a short interval of time (e.g., to monitor a cardiac intervention surgery with x-ray fluoroscopy). These conditions impose the main requirements on the reconstruction algorithm: it should produce high-quality reconstruction results with minimal artifacts under high (and possibly varying) noise levels in near real time. Our approach effectively addresses both considerations.
There have been a plethora of denoising algorithms proposed over previous decades. Most successful ones to some extent are based on the idea of parsimonious image representations in some domain that concentrates the important information in a few dimensions allowing one to efficiently separate it from isotropic noise. Such methods, however, often rely on iterative solvers, which may not be fast enough for many practical applications. Furthermore, algorithms that treat an image as a collection of its small patches produce effective high-quality results but require slow nearest-neighbor search and tend to create unwanted artifacts under high-level noise conditions. Recently, it was found that machine learning approaches successfully applied in the domain of computer vision can be adapted and produce state-of-the-art results of image reconstruction as well. It is desired to extend these ideas to produce a computationally efficient solution to the problem of image denoising.